compute_eval_marks manage onver the time evaluations

notes_tools/tools/df_marks_manip.py

@@ -294,9 +294,6 @@ def compute_eval_marks(df):
     3    E2  N2         1     4.0  01/10/2016   3.5    Total       0
 
     """
-    #exo = df[df["Question"] == "Total"]
-    #eval_pt = pd.pivot_table(exo,
-
     def date_format(dates):
         date_l = list(dates.unique())
         if len(date_l) == 1:
@@ -304,16 +301,25 @@ def compute_eval_marks(df):
         else:
             return "Trimestre"
 
-    eval_pt = pd.pivot_table(df,
-              #index = [ "Eleve", "Nom", "Date", "Trimestre"],
-              index = [ "Eleve", "Nom", "Trimestre"],
-              values = ["Bareme", "Mark", "Date"],
-              aggfunc={"Bareme": np.sum, "Mark": np.sum, "Date": date_format},
-              ).applymap(round_half_point)
+    eval_m = pd.DataFrame()
+    for eval_name in df["Nom"].unique():
+        eval_df = df[df["Nom"] == eval_name]
+        dates = eval_df["Date"].unique()
+        if len(dates) > 1:
+            # Les devoirs sur la durée, les NaN ne sont pas pénalisants
+            # On les enlèves
+            eval_df = eval_df.dropna(subset=["Mark"])
+            dates = ["Trimestre"]
+
+        eval_pt = pd.pivot_table(eval_df,
+                index = [ "Eleve", "Nom", "Trimestre"],
+                values = ["Bareme", "Mark", "Normalized", "Date"],
+                aggfunc={"Bareme": np.sum, "Mark": np.sum,"Normalized":np.mean, "Date":lambda x:dates[0]},
+                )
+        eval_pt = eval_pt.reset_index()
+        eval_m = pd.concat([eval_m, eval_pt])
+
 
-    eval_m = eval_pt.reset_index()
-    eval_m["Exercice"] = "Total"
-    eval_m["Niveau"] = 0
     return eval_m
 
 def digest_flat_df(flat_df):